资源分类
》 资源 》论文》Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions

Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions

2020-01-16 | |  63 |   43 |   0

Abstract

We present a new approach to sample from generic binary distributions, based on an exact Hamiltonian Monte Carlo algorithm applied to a piecewise continuous augmentation of the binary distribution of interest. An extension of this idea to distributions over mixtures of binary and possibly-truncated Gaussian or exponential variables allows us to sample from posteriors of linear and probit regression models with spike-and-slab priors and truncated parameters. We illustrate the advantages of these algorithms in several examples in which they outperform the Metropolis or Gibbs samplers.

上一篇：Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections

下一篇：Adaptive Submodular Maximization in Bandit Setting

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...